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Merge pull request PKU-YuanGroup#29 from PKU-YuanGroup/on_going
add parallel inference
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,119 @@ | ||
| import logging | ||
| import os | ||
| import time | ||
| import torch | ||
| import torch.distributed | ||
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| from diffusers.pipelines.consisid.consisid_utils import prepare_face_models, process_face_embeddings_infer | ||
| from diffusers.utils import export_to_video | ||
| from huggingface_hub import snapshot_download | ||
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| from xfuser import xFuserConsisIDPipeline, xFuserArgs | ||
| from xfuser.config import FlexibleArgumentParser | ||
| from xfuser.core.distributed import ( | ||
| get_world_group, | ||
| get_runtime_state, | ||
| is_dp_last_group, | ||
| ) | ||
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| def main(): | ||
| parser = FlexibleArgumentParser(description="xFuser Arguments") | ||
| args = xFuserArgs.add_cli_args(parser).parse_args() | ||
| engine_args = xFuserArgs.from_cli_args(args) | ||
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| engine_config, input_config = engine_args.create_config() | ||
| local_rank = get_world_group().local_rank | ||
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| assert engine_args.pipefusion_parallel_degree == 1, "This script does not support PipeFusion." | ||
| assert engine_args.use_parallel_vae is False, "parallel VAE not implemented for ConsisID" | ||
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| # 1. Prepare all the Checkpoints | ||
| if not os.path.exists(engine_config.model_config.model): | ||
| print("Base Model not found, downloading from Hugging Face...") | ||
| snapshot_download(repo_id="BestWishYsh/ConsisID-preview", local_dir=engine_config.model_config.model) | ||
| else: | ||
| print(f"Base Model already exists in {engine_config.model_config.model}, skipping download.") | ||
|
|
||
| # 2. Load Pipeline | ||
| device = torch.device(f"cuda:{local_rank}") | ||
| pipe = xFuserConsisIDPipeline.from_pretrained( | ||
| pretrained_model_name_or_path=engine_config.model_config.model, | ||
| engine_config=engine_config, | ||
| torch_dtype=torch.bfloat16, | ||
| ) | ||
| if args.enable_sequential_cpu_offload: | ||
| pipe.enable_sequential_cpu_offload(gpu_id=local_rank) | ||
| logging.info(f"rank {local_rank} sequential CPU offload enabled") | ||
| elif args.enable_model_cpu_offload: | ||
| pipe.enable_model_cpu_offload(gpu_id=local_rank) | ||
| logging.info(f"rank {local_rank} model CPU offload enabled") | ||
| else: | ||
| pipe = pipe.to(device) | ||
|
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| face_helper_1, face_helper_2, face_clip_model, face_main_model, eva_transform_mean, eva_transform_std = ( | ||
| prepare_face_models(engine_config.model_config.model, device=device, dtype=torch.bfloat16) | ||
| ) | ||
|
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| if args.enable_tiling: | ||
| pipe.vae.enable_tiling() | ||
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| if args.enable_slicing: | ||
| pipe.vae.enable_slicing() | ||
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| # 3. Prepare Model Input | ||
| id_cond, id_vit_hidden, image, face_kps = process_face_embeddings_infer( | ||
| face_helper_1, | ||
| face_clip_model, | ||
| face_helper_2, | ||
| eva_transform_mean, | ||
| eva_transform_std, | ||
| face_main_model, | ||
| device, | ||
| torch.bfloat16, | ||
| input_config.img_file_path, | ||
| is_align_face=True, | ||
| ) | ||
|
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||
| # 4. Generate Identity-Preserving Video | ||
| torch.cuda.reset_peak_memory_stats() | ||
| start_time = time.time() | ||
|
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| output = pipe( | ||
| image=image, | ||
| prompt=input_config.prompt[0], | ||
| id_vit_hidden=id_vit_hidden, | ||
| id_cond=id_cond, | ||
| kps_cond=face_kps, | ||
| height=input_config.height, | ||
| width=input_config.width, | ||
| num_frames=input_config.num_frames, | ||
| num_inference_steps=input_config.num_inference_steps, | ||
| generator=torch.Generator(device="cuda").manual_seed(input_config.seed), | ||
| guidance_scale=6.0, | ||
| use_dynamic_cfg=False, | ||
| ).frames[0] | ||
|
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||
| end_time = time.time() | ||
| elapsed_time = end_time - start_time | ||
| peak_memory = torch.cuda.max_memory_allocated(device=f"cuda:{local_rank}") | ||
|
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| parallel_info = ( | ||
| f"dp{engine_args.data_parallel_degree}_cfg{engine_config.parallel_config.cfg_degree}_" | ||
| f"ulysses{engine_args.ulysses_degree}_ring{engine_args.ring_degree}_" | ||
| f"tp{engine_args.tensor_parallel_degree}_" | ||
| f"pp{engine_args.pipefusion_parallel_degree}_patch{engine_args.num_pipeline_patch}" | ||
| ) | ||
| if is_dp_last_group(): | ||
| resolution = f"{input_config.width}x{input_config.height}" | ||
| output_filename = f"results/consisid_{parallel_info}_{resolution}.mp4" | ||
| export_to_video(output, output_filename, fps=8) | ||
| print(f"output saved to {output_filename}") | ||
|
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| if get_world_group().rank == get_world_group().world_size - 1: | ||
| print(f"epoch time: {elapsed_time:.2f} sec, memory: {peak_memory/1e9} GB") | ||
| get_runtime_state().destory_distributed_env() | ||
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| if __name__ == "__main__": | ||
| main() |
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